The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section
Agricultural production requires significant strategy and analysis. In many cases, agricultural growers, such as farmers or others involved in agricultural cultivation, are required to analyze a variety of data to make strategic decisions before and during the crop cultivation period. In making such strategic decisions, growers rely on computer-implemented crop yield forecast models to determine their cultivation strategy. Crop yield forecast models may help a grower decide how to spend or conserve in key areas that affect cultivation, such as fuel and resource costs, equipment investments, crop related insurance, and crop cultivation manpower.
Crop yield forecast models also are commonly used by insurance companies and risk management companies to calculate premiums based upon certain risk factors. For example, crop revenue insurance is an insurance policy that protects a farmer's projected crop revenue for a given year and covers a decline in price that occurs during the crop growing season. Such crop revenue coverage is based on determining a deviation from the mean projected revenue of the crop. For insurance companies to create profitable crop revenue insurance plans, the insurance companies must have accurate crop yield forecast models to accurately estimate the revenue of a farmer.
However, most measurements of crop production occur at the end of a growing season, and are prepared on a local or regional basis. In a large country such as the United States, obtaining accurate crop yield forecasts at the national level, and during the growing season, has been a challenge for farmers and insurance companies. Local and regional measurements are numerous and prepared in widely geographically distributed areas, and are difficult to obtain when farmers are in the growing season and occupied by other critical growing tasks. Consequently, one of the challenges in creating an accurate crop yield forecast model is simply obtaining data useful to create a national crop yield forecast model during the growing season. One approach has been to use data provided by the United States Department of Agriculture's National Agricultural Statistics Service (NASS). NASS conducts a survey-based data collection technique, where it conducts an agricultural yield survey multiple times during a year. The survey is provided directly to farmers across the country and asks the farmers to report their crop conditions at that time of year. However, this approach is not particularly useful for forecasting during the growing season because farmers are unable to provide a good estimate of their crop yield until harvest time approaches, at the end of the growing season.
Other approaches for predicting accurate crop yields during the growing season may involve using crop simulation process models, for example, to predict regional corn yields. The drawbacks to this approach are that process models require a multitude of local inputs including weather and climate conditions, soil conditions, and data points covering a large set of farming regions. These inputs then need to be calibrated in order to be accurate. The cost for collecting a high number of local inputs and calibrating the parameters make process modelling too expensive to feasibly use at a national level.
Methods for analyzing a limited number of crop related data during the growing season and modelling crop yields at a national level are desirable.